1. One Year Fellowships in AI for Drugs Discovery or AI for Neural Decoding
Fellowship in AI for Drugs Discovery : We are developing an AI system that acts like a team of scientists working together to accelerate drug discovery. Our technology uses multiple AI agents that read scientific papers, analyze medical data, and suggest new ideas for treatments - similar to how researchers brainstorm and critique each other’s work. Through our partnership with Novo Nordisk in Oxford, we’re testing this system on real drug development projects, aiming to dramatically reduce the time and cost of finding new medicines. By making drug discovery faster and more reliable, we can help bring life-saving treatments to patients sooner.
With Prof. Philip Torr
Apply here
2. Fellowship in AI for Neural Decoding:
We’re revolutionizing brain-computer interfaces by teaching AI to speak the brain’s language across multiple senses (using magnetoencephalography (MEG) and large language models as a unified foundation model). Our breakthrough approach combines brain imaging with everyday sensory data – speech, vision, and thought patterns – to create an AI system that understands neural activity more deeply than ever before (achieving SOTA metrics on neural decoding tasks). Building on Oxford’s cutting-edge research in feature universality and multi-modal neural architectures, we’re developing a unified framework that bridges the gap between human thought and machine understanding. This technology promises to transform how people with communication difficulties interact with the world, while uncovering fundamental insights about how our brains represent and process information – potentially opening new frontiers in human-computer interaction through direct neural decoding.
With Dr. Oiwi Parker Jones (PNPL 🍍) and Prof. Philip Torr
Apply here
These projects are in collaboration https://www.pillar.vc/ and https://www.aria.org.uk/
Closing: 30th April
3. Postdoctoral Research Assistant in Machine Learning
Department of Engineering Science, Central Oxford
We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is funded by EPSRC and is fixed-term to the 30th September 2026.
We are looking for great machine learning researchers to join one of three on going areas in the Torr Vision Group.
The first is on foundational work on agents, and agentic safety.
The second is on using AI in the field of genomics to aid drug discovery.
The third is explainable AI for the law.
We plan to use agent based methods to help the latter two.
In particular, we encourage and will favour applicants with a strong background in either the area of AI & genomics or in the area of .AI agents. Although those with strong backgrounds in multi modal models may be considered.
You should possess a PhD or DPhil (or near completion of) in Computer Vision or Machine Learning. You should have knowledge of approaches for areas related to efficient, reliable, and robust deep neural networks applied to computer vision tasks. You should have the ability to manage your own academic research and associated activities.
Informal enquiries may be addressed to Professor Philip Torr (email: philip.torr@eng.ox.ac.uk)
For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/
Only online applications received before midday on the 24th March 2025 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology. For more details and application, please refer to here
4. Fourth Year Project/Master Thesis
If you are a student at Oxford, have a specific research direction in mind, and aspire to publish in top conferences, please reach out to Professor Torr. philip.torr@eng.ox.ac.uk